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A Flexible State Space Model And Its Applications

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  • Hang Qian

Abstract

type="main" xml:id="jtsa12051-abs-0001"> The standard state space model treats observations as imprecise measurement of the Markovian states. Our flexible model handles the states and observations symmetrically, which are simultaneously determined by past observations and up to first-lagged states. The only distinction between the states and observations is the observability. When it is applied to the autoregressive moving average, dynamic factor and stochastic volatility models, the state space form is both parsimonious and intuitive, for low-dimension states are constructed simply by stacking all the relevant but unobserved components in the structural model.

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  • Hang Qian, 2014. "A Flexible State Space Model And Its Applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(2), pages 79-88, March.
    • Handle: RePEc:bla:jtsera:v:35:y:2014:i:2:p:79-88
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    3. Nimark, Kristoffer P., 2015. "A low dimensional Kalman filter for systems with lagged states in the measurement equation," Economics Letters, Elsevier, vol. 127(C), pages 10-13.
    4. Hauber, Philipp & Schumacher, Christian & Zhang, Jiachun, 2019. "A flexible state-space model with lagged states and lagged dependent variables: Simulation smoothing," Discussion Papers 15/2019, Deutsche Bundesbank.

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